model based systems engineer

Talent 360

<div>Overview 
Vezloo is seeking a Prompt Engineer specializing in adversarial testing of Generative AI systems. This role focuses on designing and executing structured prompt-based attack scenarios to evaluate model safety, robustness, and compliance across multiple risk domains. You will contribute to strengthening AI systems by identifying vulnerabilities and documenting model behavior under challenging inputs.
Key Responsibilities<ul><li>
Design and develop adversarial prompts across multiple risk categories, including but not limited to:
</li><li>Harmful or unsafe content generation
</li><li>Bias and fairness issues
</li><li>Misinformation and factuality manipulation
</li><li>Privacy leakage and sensitive data exposure
</li><li>Policy bypass and safety guardrail evasion
</li><li>Execute structured attack scenarios against GenAI systems, such as:
</li><li>Prompt injection techniques
</li><li>Jailbreaking attempts
</li><li>Multi-turn conversational manipulation
</li><li>Role-play and context distortion strategies
</li><li>Analyze model outputs to identify failures, inconsistencies, or policy violations
</li><li>Document findings clearly with actionable insights for model improvement
</li>Collaborate with evaluation teams to refine testing frameworks and coverage strategies 
<li>
</li></ul> 
 
Requirements 
<ul><li>
English proficiency level C1 or C2 (written and spoken)
</li><li>Proven experience in Generative AI model evaluation or assessment
</li><li>Strong hands-on experience in prompt engineering and structured prompt design
</li><li>Familiarity with adversarial testing concepts in AI systems
</li><li>Ability to think analytically and simulate edge-case user behaviors
</li><li>Access to a Mac device running the latest macOS</li></ul> 

Benefits 
<ul><li>
Fully remote work environment
</li><li>Opportunity to work on cutting-edge AI technologies
</li><li>Collaborative and inclusive team culture
</li><li>Professional growth and development opportunities</li></ul></div>

Prompt Engineer

Faze 3 Consulting

AI/ML/DevOps EngineerOwn the MLOps platform that powers enterprise AI at scale.A leading Abu Dhabi-based holding group is hiring an AI/ML/DevOps Engineer to architect, operate, and continuously improve the end-to-end MLOps and LLMOps platform for a flagship enterprise AI programme. You'll be the technical authority reviewing, governing, and signing off CI/CD, data and model pipelines, infrastructure, deployment, security, and observability &mdash; ensuring secure, scalable, and compliant delivery across environments. Reports to the AI Product Manager within the AI Excellence Centre.What you'll own:<ul><li>Own the end-to-end MLOps/LLMOps reference architecture: ingestion &rarr; validation &rarr; feature and embedding pipelines &rarr; training and fine-tuning &rarr; evaluation &rarr; registry &rarr; deployment &rarr; monitoring &mdash; including RAG and agentic workflows.</li><li>Architect, review, and approve CI/CD for ML and LLM systems: code, data, prompt, and model artifact versioning; build and release pipelines (Azure DevOps / GitHub Actions); automated unit, integration, and contract testing; and promotion/rollback (blue-green / canary) across dev, test, and production.</li><li>Define and govern AI platform foundations on Azure: IaC (Bicep/Terraform), AML workspaces, AKS GPU node pools and scheduling, private networking (VNet integration / Private Link), identity (Managed Identities / PIM), secrets (Key Vault), and encryption and data residency controls.</li><li>Review and approve production deployment patterns for model and LLM serving (AKS / KServe / AML online endpoints), including containerization, inference optimization (batching, quantization where applicable), API management, autoscaling, resiliency, and RAG runtime components (vector store, retriever, re-ranker, cache).</li><li>Own observability and reliability for AI services: OpenTelemetry tracing, prompt and inference logs (with PII controls), latency/throughput/cost metrics, SLOs/SLIs, model performance monitoring, data and model drift detection, and LLM evaluations (quality, hallucination checks, toxicity and safety guardrails) with incident playbooks.</li><li>Establish and enforce MLOps/LLMOps governance: dataset lineage, data quality validation (schema and tests), feature store and model registry standards, artifact provenance (SBOM/SLSA), vulnerability scanning, approval gates for model and prompt releases, and compliance-aligned documentation for model risk (intended use, limitations, evaluation results).</li><li>Enable delivery squads &mdash; including the primary delivery partner &mdash; with &quot;golden path&quot; templates (AML pipelines, RAG blueprints, evaluation harnesses), reusable IaC modules, and coding standards; run deep technical design and architecture reviews and sign off production readiness (capacity, security, observability, DR) for all AI releases.</li><li>Support the Run &amp; Operate model by enabling issue triage and minor enhancement workflows (ticket intake &rarr; fix &rarr; controlled release), ensuring changes follow the same release governance and quality gates.</li><li>Own the Operational Acceptance Gate: no production release without runbooks, monitoring dashboards, incident playbooks, access model, and DR test evidence.</li></ul>Scope clarity: you provide platform standards, review, and sign-off &mdash; you do not replace the delivery partner's engineering, but you enforce the &quot;golden path&quot; and production readiness bar.What you bring:<ul><li>8&ndash;10 years across DevOps, SRE, and/or ML Engineering with production systems on Azure.</li><li>Hands-on experience with Azure ML, AKS, Azure DevOps or GitHub Actions, IaC, and containerization.</li><li>Bachelor's in Computer Science, Engineering, or equivalent experience.</li></ul>Core skills required:<ul><li>Python, YAML, Docker, Helm, KQL; GitOps (Argo/Flux) awareness.</li><li>Security in CI/CD: SAST/DAST, supply-chain security (Sigstore), secrets management (Key Vault).</li><li>Performance testing (k6 / JMeter), contract testing, and E2E testing.</li><li>Cost optimization and capacity planning for GPU and CPU workloads.</li><li>Strong grasp of model serving, inference optimization, and observability tooling.</li></ul>Required certification:<ul><li>Microsoft Certified: DevOps Engineer Expert (AZ-400)</li></ul>Preferred certifications:<ul><li>Microsoft Certified: Azure Administrator (AZ-104) or Solutions Architect (AZ-305)</li><li>CKA or CKAD (Kubernetes)</li></ul>Location: Abu Dhabi, UAEEmployment Type: Permanent, Full-timeExperience: 8&ndash;10 yearsSalary Range: 25,000 - 33,000 (AED per month)Pay: AED25,000.00 - AED33,000.00 per monthWork Location: In person

AI/ML/DevOps Engineer

Zoolatech

<div><div><div><div><div><div><div><div>OVERVIEW</div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div>ATRC's Data &amp; AI Office is a product and platform execution team &mdash; not a traditional enterprise data function. We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.
What we are building &mdash; now:<ul><li>
An executive reporting portal serving senior leadership, integrating live data from SAP, Microsoft 365, SuccessFactors, and internal systems across multiple operational views (finance &amp; budget, KPIs, technology portfolio, news, wellbeing, and a personalized executive home)</li><li>
A sovereign data and AI platform: data warehouse, governed pipelines, metadata catalog, and classification enforcement across all entities</li><li>
Data mesh architecture with federated ownership &mdash; each entity owns its data products; the platform provides governed access</li><li>
Data readiness for the organization's agentic AI platform &mdash; clean, structured, governed data so AI agents can operate on trusted sources</li><li>
Operational AI agents delivering real value on top of the agentic platform (document triage, anomaly detection, automated briefings, classification automation)</li><li>
AI-enabled situational awareness and crisis management systems</li><li>
Data governance and classification &mdash; managing an active external vendor engagement, bridging framework deliverables into platform-enforced rules, and building the ongoing
governance operations the organization requires</li></ul>
How we work:<ul><li>
Small team, high ownership &mdash; every engineer owns a module end-to-end</li><li>
Two-week sprints with demonstrated output at every review</li><li>
Ship and improve &mdash; working systems beat perfect designs</li><li>
Core team owns all architecture and product decisions; external capacity executes within defined scope</li><li>
Decisions take hours, not weeks. Daily technical choices are made by the engineer.</li><li>
Feature scope decisions take 24 hours. Architecture pivots take 48 hours. Nothing sits in limbo.</li><li>
Oversee 5 other workstreams powered by 3rd party vendors within the ecosystem but whom we are ultimately accountable for</li></ul>
</div><div>Builds the AI capabilities that operations depend on &mdash; classification automation, production agents, and the data bridge to the agentic platform.
Mission
Build, own, and continuously improve the AI capabilities across the office's production systems, working under the direction of the Principal AI Engineer. Your scope: automated data classification (remediating what's currently broken), AI-generated briefings, and operational AI agents that deliver real value in weeks.
An external partner is building an agentic platform that ATRC will use. Your role in that relationship: define the data contracts the platform consumes, evaluate the sample agents delivered, and build the production agents on top of the platform that go beyond demonstrations into real operational use. You don't build the platform &mdash; you make sure ATRC's data is ready for it and that what gets built on top of it actually works.
What This Role Owns<ul><li>
Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance</li><li>
Lead Operational AI agents: building production agents on top of the agentic platform &mdash; going beyond the sample agents the external partner delivers into real operational workflows</li><li>
Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees &mdash; working with the Principal AI Engineer</li><li>
AI service implementation: FastAPI service around LLM APIs with versioned prompt templates</li><li>
Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution</li><li>
Prompt versioning: templates in configuration, editable without code changes</li><li>
Observability: every LLM call logged with input hash, model version, output, latency, token count</li><li>
Fallback logic: graceful degradation when LLM APIs are unavailable</li><li>
Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts</li></ul>
Key Decisions<ul><li>
Prompt implementation &mdash; few-shot examples, output parsing, error handling</li><li>
Confidence threshold tuning based on evaluation results</li><li>
Context window packing &mdash; what data fits within token budgets</li><li>
Fallback behavior when APIs degrade</li><li>
Agent evaluation &mdash; whether an external partner's sample agent is production-ready or needs rework</li><li>
Bug triage for AI components &mdash; autonomous fix vs. escalate to Principal AI Engineer</li></ul>
Does Not Do<ul><li>
Define AI architecture or agent design patterns (Principal AI Engineer)</li><li>
Build the agentic platform itself (external partner)</li><li>
Build the backend API or data pipelines</li><li>
Fine-tune or train models</li><li>
Define governance policies (Governance Lead)</li></ul>
Ideal Candidate
Has shipped an LLM-based feature that non-technical users depend on daily &mdash; and has been responsible when it breaks. Knows the hardest part of applied AI is the fallback, the observability, and the human review loop &mdash; not the prompt. Can evaluate someone else's agent demo and quickly identify whether it's production-ready or held together with string. Works well under a senior AI lead &mdash; takes architectural direction and executes with high quality and speed. Not attached to a particular model &mdash; the job is reliable output.
</div><div><ul><li>LLM APIs (Claude, GPT-4, open-weight models) &mdash; structured output, JSON mode, system prompts</li><li>
Prompt engineering for classification &mdash; zero-shot and few-shot</li><li>
Python &mdash; async API calls, retry logic, exponential backoff</li><li>
LLM evaluation &mdash; precision/recall, human-AI agreement scoring</li><li>
Structured output &mdash; JSON schema enforcement, Pydantic validation</li><li>
Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)</li><li>
Token budgeting and context window management</li><li>
AI observability &mdash; output quality monitoring, anomaly detection</li><li>
FastAPI and Docker</li></ul>
</div></div></div><div><div><div><div>Location:
<div>Istanbul, Turkey
</div></div><div>Seniority:
<div>Senior
</div></div><div>Technologies:
<div>Python
</div></div></div></div><div><div>Benefits:
<ul><li>Paid Vacation
</li><li>Hybrid Work (home/office)
</li><li>Sick Days
</li><li>Sport/Insurance Compensation
</li><li>Holidays Day Off
</li><li>English Classes
</li><li>Training Compensation
</li><li>Transportation compensation</li></ul></div></div></div></div></div></div>

AI Engineer

DeepSource Technologies

<div>We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.
This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.
Key Responsibilities<ul><li>Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.</li><li>Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.</li><li>Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.</li><li>Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.</li><li>Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.</li><li>Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.</li><li>Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.</li><li>Ensure reproducibility, reliability, and version control across AI/ML environments.</li><li>Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.</li><li>Build repeatable environments using Infrastructure as Code.</li><li>Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.</li><li>Design scalable inference and serving patterns for variable workloads.</li><li>Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.</li><li>Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.</li><li>Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.</li><li>Deliver analytics-ready datasets to downstream systems and applications.</li><li>Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.</li><li>Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.</li><li>Contribute to architecture decisions, technical documentation, best practices, and engineering standards.</li><li>Support knowledge sharing, technical leadership, and continuous improvement across the AI function. 
</li></ul> 
 
Requirements<ul><li> 
Bachelor&rsquo;s degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, or a related field.</li><li>5+ years of hands-on experience in AI engineering, machine learning engineering, MLOps, or data/ML platform engineering.</li><li>Proven experience deploying production AI/ML solutions in enterprise environments.</li><li>Strong programming experience in Python and SQL.</li><li>Strong experience with enterprise AI/ML architecture and delivery.</li></ul></div>

AI Architect - Riyadh,KSA

Quantum HR

<div>About Us
Quantum HR is a premier human resources consulting firm dedicated to connecting exceptional talent with leading organizations worldwide. We specialize in providing bespoke recruitment solutions, leveraging deep industry insights and a global network to help companies build high-performing teams by matching them with professionals who truly fit their needs and culture. 
 
About the Role
We are hiring a Machine Learning Engineer to design, build, and deploy advanced ML models powering Digified&rsquo;s digital identity verification and contracting platforms. 
You will work on real-world, high-impact problems in computer vision, biometrics, OCR, fraud detection, and NLP, delivering production-grade ML systems integrated into APIs and enterprise workflows.
This is a hands-on engineering role focused on taking models from research to scalable, secure, and compliant production environments. 
 
Key Responsibilities 
<ul><li>
Develop and optimize ML models for face recognition, liveness detection, OCR, fraud detection, and NLP-based document understanding
</li><li>Build and maintain production-grade ML pipelines and APIs for real-time inference
</li><li>Deploy and scale models using MLOps best practices (CI/CD, versioning, monitoring, retraining)
</li><li>Optimize model performance for latency, accuracy, and reliability in production environments
</li><li>Design and manage high-quality datasets with proper annotation and data governance
</li><li>Monitor model performance (drift, accuracy, false acceptance/rejection rates)
</li><li>Collaborate with backend, mobile, DevOps, and product teams to integrate ML systems
</li><li>Ensure secure, compliant ML practices aligned with regulatory and data privacy standards
</li><li>Conduct research and propose improvements in model architectures and system performance</li></ul> 

Requirements 
<ul><li>
3&ndash;7 years of experience in Machine Learning, Deep Learning, or Computer Vision
</li><li>Strong Python programming skills
</li><li>Experience with PyTorch and/or TensorFlow/Keras
</li><li>Strong background in at least one of: Computer Vision, NLP, or Fraud Detection
</li><li>Experience with ML deployment (FastAPI, Flask, Docker, Kubernetes)
</li><li>Understanding of MLOps principles (model versioning, monitoring, retraining pipelines)
</li><li>Solid foundation in mathematics (linear algebra, probability, statistics)
</li><li>Experience working with production ML systems 
 
Nice to Have 
</li><li>
Experience with face recognition, OCR, or liveness detection systems
</li><li>Exposure to Vision Transformers, Siamese Networks, CRNNs, or LLM-based systems
</li><li>Experience with distributed training or GPU acceleration
</li><li>Familiarity with MLflow or experiment tracking tools
</li><li>Knowledge of identity standards (FIDO, NIST 800-63-3)
</li><li>Background in fintech, regtech, or anti-fraud systems
</li><li>Experience with trust &amp; safety or government-grade systems</li></ul> 

Benefits 
<ul><li>
Work on cutting-edge ML systems in digital identity and fraud prevention
</li><li>High-impact role with production ownership from research to deployment
</li><li>Exposure to enterprise-grade AI systems used in regulated environments
</li><li>Strong engineering culture with cross-functional collaboration
</li><li>Opportunity to work on advanced CV/NLP/biometric systems at scale
</li><li>Career growth in a fast-scaling AI-driven product company</li></ul></div>

ML Engineer

Tamara

<div>About Us

Tamara is the leading fintech platform in Saudi Arabia and the wider GCC region with a mission to help people make their dreams come true by building the most customer-centric financial super-app on earth. The company serves millions of users in the region and partners with leading global and regional brands such as SHEIN, Jarir, noon, IKEA and Amazon, as well as small and medium businesses.

Tamara is Saudi Arabia&rsquo;s first fintech unicorn and is backed by Sanabil Investments, a wholly owned company by the Public Investment Fund (PIF), SNB Capital, Checkout.com, amongst others. The company operates from its headquarters in Riyadh, with additional regional and global support offices.

Your Role

We are looking for a Senior Machine Learning Engineer (MLE) to join our Risk Data Science team. You will play a key role in designing, building, deploying, and scaling ML models that drive credit risk, fraud prevention, behavioral scoring, and other risk-related decision systems across our business.

You will work closely with data scientists, risk analysts, and engineering teams to transform research prototypes into high-performance, production-grade solutions that operate at scale in real-time decisioning environments.

Your Responsibilities

Model Deployment &amp; Scaling 
<ul>
<li>Productionise risk and fraud models developed by the DS team using robust, efficient, and maintainable architectures</li>
<li>Design low-latency, high-availability APIs and pipelines for real-time model inference.</li>
<li>Implement batch scoring systems for periodic risk assessments.=</li>
</ul>
MLOps &amp; Infrastructure
<ul>
<li>Build and maintain CI/CD pipelines for model deployment and monitoring.</li>
<li>Set up automated feature engineering pipelines, leveraging feature stores.</li>
<li>Ensure model governance: reproducibility, versioning, auditability, and compliance with regulatory requirements.</li>
</ul>
Model Monitoring &amp; Maintenance
<ul>
<li>Implement real-time and batch monitoring for data drift, concept drift, and model performance.</li>
<li>Build automated retraining workflows and model rollback mechanisms.</li>
</ul>
Collaboration with Risk DS
<ul>
<li>Work closely with risk data scientists to translate experimental code (Python, notebooks) into production-grade services.</li>
<li>Advise DS on efficient model architectures for operational environments.</li>
<li>Optimize feature computation for speed and scalability.</li>
</ul>
System Design &amp; Integration
<ul>
<li>Integrate models with credit underwriting, fraud detection, collections, and merchant risk systems.</li>
<li>Collaborate with backend engineering to align on API contracts and system interfaces.</li>
</ul>
Your Expertise
<ul>
<li>6+ years of experience as an MLE, ML Engineer, Mlops Developer.</li>
<li>Strong Python skills (including Pandas, NumPy, scikit-learn, PySpark, FastAPI/Flask).</li>
<li>Proficiency in distributed computing frameworks (Spark, Ray) and workflow orchestration tools (Airflow, Prefect).</li>
<li>Experience with MLOps tools (MLflow, SageMaker, Vertex AI, or similar).</li>
<li>Strong understanding of model deployment in cloud environments (AWS/GCP/Azure).</li>
<li>Solid knowledge of microservice architecture, containerization (Docker), and orchestration (Kubernetes).</li>
<li>Proven track record of deploying and maintaining ML models in production at scale.</li>
<li>Experience in building and integrating with real-time streaming systems (Kafka, Kinesis, Pub/Sub).</li>
</ul>
All qualified individuals are encouraged to apply.</div>

Machine Learning Engineer

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